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cognitive_architectures_language_agents [2026/03/24 17:56] – Create page with researched content on CoALA framework agentcognitive_architectures_language_agents [2026/03/24 17:58] (current) – Create page: CoALA framework - cognitive architectures for language agents agent
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 ====== Cognitive Architectures for Language Agents (CoALA) ====== ====== Cognitive Architectures for Language Agents (CoALA) ======
  
-The **Cognitive Architectures for Language Agents (CoALA)** framework, introduced by Sumers et al. (2023), provides a systematic taxonomy for organizing and understanding language agents built on large language models. Drawing deeply from cognitive science and classical AI architectures such as Soar, CoALA decomposes agents into modular components: memory systems, action spaces, and decision-making procedures. This principled decomposition enables both retrospective analysis of existing agents (e.g., ReAct, Reflexion) and prospective identification of underexplored design dimensions.+The **Cognitive Architectures for Language Agents (CoALA)** framework, proposed by Sumers et al. (2023), provides a systematic taxonomy for organizing LLM-based language agents into modular components inspired by cognitive science. Drawing on decades of research in cognitive architectures such as Soar and ACT-R, CoALA formalizes the design space of language agents through memory modules, structured action spaces, and decision-making procedures.
  
-===== Formal Agent Definition =====+===== Overview =====
  
-CoALA formalizes a language agent as a tuple:+As language model-based agents proliferate --- from ReAct to Reflexion to Voyager --- the field lacks a unifying framework to compare, categorize, and design them. CoALA addresses this by proposing a modular architecture that retrospectively organizes existing agents and prospectively identifies gaps in the design space. The framework defines an agent as a tuple:
  
 $$A = (M_w, M_{lt}, \mathcal{A}_i, \mathcal{A}_e, D)$$ $$A = (M_w, M_{lt}, \mathcal{A}_i, \mathcal{A}_e, D)$$
  
-where $M_w$ denotes working memory, $M_{lt}$ long-term memory, $\mathcal{A}_i$ internal actions, $\mathcal{A}_e$ external actions, and $D$ the decision-making procedure. The LLM serves as the core computational engine that processes and transforms information across these components.+where $M_w$ is working memory, $M_{lt}$ is long-term memory, $\mathcal{A}_i$ is the internal action space, $\mathcal{A}_e$ is the external action space, and $D$ is the decision procedure.
  
-===== Memory Systems =====+===== Memory Modules =====
  
-CoALA divides agent memory into short-term and long-term stores, mirroring distinctions from cognitive psychology:+CoALA divides agent memory into **working memory** and three types of **long-term memory**, mirroring distinctions from cognitive psychology:
  
-**Working Memory** ($M_w$): A short-term scratchpad holding the agent's current context — recent observations, partial plans, and intermediate reasoning outputs. This is analogous to the limited-capacity buffer in human cognition. +  * **Working Memory**: A short-term scratchpad holding the agent's current context --- recent observations, intermediate reasoning results, and partial plans. Analogous to the limited-capacity buffer in human cognition. 
- +  * **Episodic Memory**: Stores past experiences and events (e.g., "What happened when I tried approach X?"). Enables learning from specific interaction histories. 
-**Long-Term Memory** ($M_{lt}$) is subdivided into three modules: +  * **Semantic Memory**: Holds factual world knowledge (e.g., "Water boils at 100°C at sea level"). Can be stored as text, embeddings, or knowledge graphs. 
- +  * **Procedural Memory**: Encodes skills and procedures --- often represented as code snippets, tool definitions, or implicitly within LLM parameters. Defines //how// to perform actions.
-  * **Episodic Memory**: Stores records of past experiences and events (e.g., "What happened when I tried approach X last time?"). Enables learning from history. +
-  * **Semantic Memory**: Holds factual world knowledge in structured or unstructured form (e.g., knowledge graphs, document stores). Provides the agent's understanding of the world. +
-  * **Procedural Memory**: Encodes skills and procedures, often as executable code or as knowledge implicitly stored in LLM parameters. Governs //how// the agent acts.+
  
 ===== Action Spaces ===== ===== Action Spaces =====
  
-Actions are partitioned into internal operations on memory and external interactions with the environment:+Actions are partitioned into **internal** and **external** categories:
  
-**Internal Actions** ($\mathcal{A}_i$): +=== Internal Actions === 
-  * **Retrieval**: Reading from long-term memory into working memory +  * **Retrieval**: Reading from long-term memory stores 
-  * **Reasoning**: Updating working memory via LLM inference (chain-of-thought, planning) +  * **Reasoning**: Updating working memory via LLM inference (chain-of-thought, reflection) 
-  * **Learning**: Writing new information from working memory into long-term memory+  * **Learning**: Writing new information to long-term memory
  
-**External Actions** ($\mathcal{A}_e$): +=== External Actions === 
-  * **Grounding**: Interacting with the outside world — tool use, API calls, web browsing, robotic control +  * **Grounding**: Interacting with the outside world --- tool use, API calls, web browsing, or robotic control
- +
-===== Decision-Making Procedure ===== +
- +
-The decision procedure $D$ operates as a continuous loop with two alternating stages: +
- +
-  - **Planning Stage**: Iteratively applies reasoning and retrieval actions to propose, evaluate, and select a grounding or learning action. The LLM generates candidate plans, retrieves relevant memories, and scores alternatives. +
-  - **Execution Stage**: Performs the selected external action, receives environmental feedback, updates working memory with new observations, and re-enters the planning stage. +
- +
-This creates the characteristic perceive-think-act cycle that distinguishes agents from single-pass LLM inference.+
  
 <code python> <code python>
-# Simplified CoALA decision loop+# Simplified CoALA agent loop
 class CoALAAgent: class CoALAAgent:
     def __init__(self, llm, episodic_mem, semantic_mem, procedural_mem):     def __init__(self, llm, episodic_mem, semantic_mem, procedural_mem):
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         self.procedural = procedural_mem         self.procedural = procedural_mem
  
-    def step(self, observation):+    def decision_loop(self, observation):
         self.working_memory.append(observation)         self.working_memory.append(observation)
-        # Internal actions: retrieval + reasoning +        while not self.should_act_externally(): 
-        context = self.semantic.retrieve(observation) +            # Internal actions: retrieve, reason, learn 
-        past = self.episodic.retrieve(observation) +            retrieved = self.retrieve(self.working_memory) 
-        self.working_memory.extend([context, past]) +            reasoning = self.llm.reason(self.working_memory + retrieved) 
-        # Decision via LLM reasoning +            self.working_memory.append(reasoning) 
-        plan = self.llm.reason(self.working_memory) +        action = self.select_external_action(self.working_memory) 
-        # External action: grounding +        result = self.execute(action) 
-        result = self.execute(plan.action) +        self.episodic.store(observation, action, result)
-        # Learning: store experience +
-        self.episodic.store(observation, plan, result)+
         return result         return result
 </code> </code>
  
-===== Connections to Cognitive Science =====+===== Decision Procedures ===== 
 + 
 +CoALA formalizes decision-making as a continuous loop with two stages: 
 + 
 +  - **Planning Stage**: The agent iteratively applies reasoning and retrieval to propose, evaluate, and select actions. This may involve multi-step deliberation or simple reactive mappings. 
 +  - **Execution Stage**: The selected action is performed (grounding or learning), the environment returns new observations, and the cycle repeats. 
 + 
 +This distinguishes agents on a spectrum from purely **reactive** (single LLM call maps observation to action) to **deliberative** (multi-step internal planning before acting).
  
-CoALA explicitly builds on classical cognitive architectures, particularly **Soar** (Laird, Newell & Rosenbloom, 1987), where production rules in long-term memory match against working memory to fire actions. CoALA replaces rigid production rules with flexible LLM-based reasoning while retaining the modular memory-action-decision structure. The framework also draws on Tulving's (1972) distinction between episodic and semantic memory and Anderson's (1983) ACT-R model of procedural knowledge.+===== Connections to Cognitive Science =====
  
-This grounding in cognitive science enables CoALA to serve as a bridge between decades of cognitive architecture research and the emerging field of LLM-based agents, providing a common vocabulary for comparing disparate systems.+CoALA explicitly builds on classical cognitive architectures:
  
-===== Key Insights =====+  * **Soar**: Production rules in long-term memory match working memory contents to trigger actions. CoALA replaces symbolic productions with LLM-based reasoning. 
 +  * **ACT-R**: Distinguishes declarative and procedural memory with activation-based retrieval. CoALA's memory taxonomy mirrors this structure. 
 +  * **Global Workspace Theory**: Working memory serves as a shared workspace where different modules contribute and compete for attention.
  
-The survey of existing agents through the CoALA lens reveals that most current systems underutilize long-term memory — particularly episodic and procedural memory. Many agents rely solely on in-context learning (working memory) without persistent storage, limiting their ability to improve over time.+The framework positions LLM agents within a 50-year lineage of AI research, arguing that cognitive architectures provide the missing organizational structure for the rapidly expanding space of language agents.
  
 ===== References ===== ===== References =====
  
   * [[https://arxiv.org/abs/2309.02427|Sumers et al. "Cognitive Architectures for Language Agents" (2023)]]   * [[https://arxiv.org/abs/2309.02427|Sumers et al. "Cognitive Architectures for Language Agents" (2023)]]
-  * [[https://arxiv.org/abs/2210.03629|Yao et al. "ReAct: Synergizing Reasoning and Acting" (2022)]] +  * [[https://arxiv.org/abs/2210.03629|Shinn et al. "Reflexion: Language Agents with Verbal Reinforcement Learning" (2023)]] 
-  * [[https://arxiv.org/abs/2303.11366|Shinn et al. "Reflexion: Language Agents with Verbal Reinforcement Learning" (2023)]]+  * [[https://arxiv.org/abs/2210.02414|Yao et al. "ReAct: Synergizing Reasoning and Acting in Language Models" (2023)]]
  
 ===== See Also ===== ===== See Also =====
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